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Hotbeds of crime and the search for spatial accuracy

Author

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  • J. H. Ratcliffe

    (School of Geography, University of Nottingham, University Park Nottingham NG7 2RD, UK (e-mail: michael.mccullagh@nottingham.ac.uk))

  • M. J. McCullagh

    (School of Geography, University of Nottingham, University Park Nottingham NG7 2RD, UK (e-mail: michael.mccullagh@nottingham.ac.uk))

Abstract

. One of the most important aspects of spatial crime analysis is the identification of hotspots: areas of the highest crime concentration. This paper advances a methodology for hotspot detection based on a global moving window approach combined with the use of local statistics to define the hotspot limit. This technique generates hotspots that both follow the urban morphology of the crime distribution and ensures their spatial segregation. The hypothesis that police officers can construct an accurate perception of crime distribution from exposure to daily policing practices is used to demonstrate an application in the use of hotspot analysis. Significant regions generated from recorded crime data are compared with perceived local hotspots catalogued from surveys with police officers. Results from this study show two discrete types of hotspot, here termed hotpoints and hotbeds. The morphology of these crime hotpoints and hotbeds is discussed and possible causes documented.

Suggested Citation

  • J. H. Ratcliffe & M. J. McCullagh, 1999. "Hotbeds of crime and the search for spatial accuracy," Journal of Geographical Systems, Springer, vol. 1(4), pages 385-398, December.
    • Handle: RePEc:kap:jgeosy:v:1:y:1999:i:4:d:10.1007_s101090050020
    DOI: 10.1007/s101090050020
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    Citations

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    Cited by:

    1. Massimo Craglia & Robert Haining & Paola Signoretta, 2001. "Modelling High-intensity Crime Areas in English Cities," Urban Studies, Urban Studies Journal Limited, vol. 38(11), pages 1921-1941, October.
    2. Corcoran, Jonathan J. & Wilson, Ian D. & Ware, J. Andrew, 2003. "Predicting the geo-temporal variations of crime and disorder," International Journal of Forecasting, Elsevier, vol. 19(4), pages 623-634.
    3. Lin, Xu & Zhang, Jihu & Jiang, Shanhe, 2022. "Spatial and temporal correlations of crime in Detroit: Evidence from spatial dynamic panel data models," International Review of Law and Economics, Elsevier, vol. 72(C).
    4. Robert Haining & Jane Law, 2007. "Combining police perceptions with police records of serious crime areas: a modelling approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1019-1034, October.
    5. Walsh, Jeffrey A. & Taylor, Ralph B., 2007. "Community structural predictors of spatially aggregated motor vehicle theft rates: Do they replicate?," Journal of Criminal Justice, Elsevier, vol. 35(3), pages 297-311.
    6. Robert Johnston & Mahesh Ramachandran, 2014. "Modeling Spatial Patchiness and Hot Spots in Stated Preference Willingness to Pay," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(3), pages 363-387, November.
    7. Jane Law & Matthew Quick, 2013. "Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach," Journal of Geographical Systems, Springer, vol. 15(1), pages 89-113, January.
    8. Amanda S. Hering & Sean Bair, 2014. "Characterizing spatial and chronological target selection of serial offenders," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 123-140, January.
    9. Andresen, Martin A., 2011. "Estimating the probability of local crime clusters: The impact of immediate spatial neighbors," Journal of Criminal Justice, Elsevier, vol. 39(5), pages 394-404.
    10. Johnston, Robert J. & Ramachandran, Mahesh & Schultz, Eric T. & Segerson, Kathleen & Besedin, Elena Y., 2011. "Characterizing Spatial Pattern in Ecosystem Service Values when Distance Decay Doesn’t Apply: Choice Experiments and Local Indicators of Spatial Association," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 103374, Agricultural and Applied Economics Association.
    11. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
    12. Oh, Gyeongseok & Ren, Ling & He, Phil, 2019. "Social disorder and residence-based fear of crime: The differential mediating effects of police effectiveness," Journal of Criminal Justice, Elsevier, vol. 63(C), pages 1-11.
    13. Juan Andrés Cabral, 2021. "Understanding unsolved crimes hotspots: a spatial approach," Asociación Argentina de Economía Política: Working Papers 4445, Asociación Argentina de Economía Política.

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